ArticleIndian journal of psychological medicine2026
Benchmarking Large Language Models Against Psychiatry Residents Using Traditional Institutional Assessments.
Article in Indian journal of psychological medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) models demonstrate remarkable capabilities in healthcare applications, yet their performance compared to medical trainees in psychiatric education remains unexplored. This study evaluated the comparative performance of large language models (LLMs) against first-year psychiatry residents in standardized assessments at a premier Indian medical educational institute. Methods: For this study, the already-scored answer sheets for Theory Papers I and II, as well as unmanned, non-interactive Objective Structured Clinical Examinations (OSCEs) with image-based tasks, from all 25 first-year psychiatry residents (March 2024 exam) were obtained from the examination section of the institute. The same question papers were then uploaded into three AI models (ChatGPT Results: AI models outperformed residents in theoretical assessments. In Paper I (theory), AI models achieved mean scores (standard deviation) of Claude Sonnet 67.88 (10.63), ChatGPT Conclusions: While AI demonstrated superior theoretical knowledge, equivalent or variable practical skills performance reveals fundamental limitations in clinical reasoning and contextual understanding. These findings necessitate reconceptualizing psychiatric education to emphasize uniquely human competencies while leveraging AI's capabilities for knowledge synthesis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.